Dear Christoph, Ron, and Jack, (01)
CL
> In short: facing the reality of Big Data and the need for increasingly
> intelligent services, (how) can ontology-based tools and techniques help? (02)
That question gets to the heart of the matter. (03)
But before we can answer the question "How?", we need to ask what
we mean by "ontology-based tools" and whether the current tools
that use the tag 'ontology' are the most relevant to ontology. (04)
RW
> The IBM team provided Watson with millions of documents, including
> dictionaries, encyclopedias, and other reference material that it
> could use to build its knowledge. Although Watson was not connected
> to the Internet during the game, it contained 200 million pages of
> structured and unstructured content consuming four terabytes of
> disk storage, including the full text of Wikipedia. (05)
That is using "Big Data" to tackle the problem of "Big Data".
The following diagram from Wikipedia summarizes the steps in
Watson's approach to answering a question: (06)
http://upload.wikimedia.org/wikipedia/commons/thumb/4/41/DeepQA.svg/800px-DeepQA.svg.png (07)
None of the boxes in that diagram explicitly mention ontology. Among
the many resources, they do use DBpedia, which does use RDF and OWL. (08)
But the classifications used for DBpedia don't use features of OWL
that go beyond Aristotle's syllogisms. An automated or at least semi-
automated system that generates hierarchies without all the hand coding
of OWL could be more useful -- for example, Formal Concept Analysis: (09)
http://www.upriss.org.uk/fca/fca.html (010)
Note the applications of FCA to construct lattices automatically from
resources such as WordNet and Roget's Thesaurus. FCA has also been
used to check the consistency of OWL ontologies. But if you can derive
the hierarchy automatically, why bother with a method that depends on
hand-coding, such as OWL? (011)
JR
> Once you have enabled a semantic model of me then a machine can find
> any and all instances in Big Data that are relevant to me... (012)
Yes. But it's important to have automated or semi-automated ways of
creating such models or deriving them from the data. I've mentioned
the kinds of things we've been doing at VivoMind. For a reminder,
see http://www.jfsowa.com/talks/goal7.pdf (013)
But to show other systems that also do similarity matches by automated
methods, see the excerpt below. Note that they use a triple store,
but they create an associative memory that retrieves information by
similarity. That's very different from the typical SW tools. (014)
Some people say "Tools are boring." I agree that YASWT (Yet Another
Semantic Web Tool) is indeed boring. But the new tools implement
ways of thinking about ontology that are totally different from the
SW dogma. That is not boring. That can be revolutionary. (015)
Given that people have been spending 14 years to "educate" software
developers about ontology, maybe we need some new thinking. (016)
John
_____________________________________________________________________ (017)
https://saffrontech.atlassian.net/wiki/display/saffron/The-SMB-Store (018)
The SMB (SaffronMemoryBase) Store is a different kind of data store.
Sometimes referred to as an information or knowledge store rather than a
data store because it stores weighted "associations" or "links" between
things, the fundamental representation of knowledge. Similar to how a
search (inverted) index stores a link between a keyword and a document,
SMB also stores links between terms. However, SMB goes beyond storing
links between keywords and documents. Like RDF (triple) stores, SMB can
also store associations between 3 things. But again, SMB goes beyond
just storing links between 3 things. SMB also stores association
counts, i.e. the number of times a particular association has occurred.
Associations are typically, but not limited to, stored as doubles
(A-C) and/or triples (A-B-C), where triples provide additional context
(B) to the link (A-C). Looking up association counts, at query time,
allow you to very efficiently utilize frequency-based statistics to
compute results. When compared with alternative database methods of
computing association counts at query time, you will find that using a
SaffronMemoryBase makes the impractical, practical and opens a new world
of opportunities for innovative problem solving. (019)
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